通过自编码器解读健康和精神病中的大脑结构-功能动态.
Qing Cai1,2, Hannah Thomas3,4, Vanessa Hyde1,5
1Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff, UK.
Scientific reports
|November 14, 2025
概括
深度学习模型有效地绘制了大脑结构的功能,在预测功能连接方面表现优于分析方法. 这揭示了精神病中明显的结构-功能合,突出显示了大脑通信模式的改变.
科学领域:
- 神经科学是一个神经科学.
- 脑部成像 脑部成像
- 计算精神病学是一种计算精神病学.
背景情况:
- 了解大脑的结构功能关系对于神经科学和精神病学至关重要.
- 扩散核磁共振和磁脑电图是分别测量结构和功能连接性的关键技术.
研究的目的:
- 开发和比较一个深度学习模型和一个分析模型来映射结构连接到功能连接.
- 研究健康个体和患有精神病的个体的结构功能合.
主要方法:
- 使用图形多头注意力自编码器 (深度学习) 将扩散MRI衍生的结构连接映射到磁脑学衍生的功能连接.
- 使用最短路径长度和搜索信息的分析模型用于比较.
- 这两种模型都应用于健康参与者和精神病患者的数据.
主要成果:
- 深度学习模型在预测健康个体的功能连接性方面取得了更高的准确性 (相关系数>0.8),与分析模型 (0.45) 相比,在α和β频段实现了更高的准确性.
- 与健康对照人群相比,在精神病患者中观察到明显的结构-功能合模式.
- 结构-功能关系的变化在精神病中比结构或功能特定的变化更明显.
结论:
- 人类大脑的结构和电生理功能连接密切相关.
- 深度学习模型提供了一种更复杂的方法来理解大脑动态,特别是在更高频段.
- 这项研究强调了先进的计算模型在识别精神病症等精神疾病中神经变化的实用性.
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